Skip to main content
SpringerLink
Log in

Priority based data gathering using multiple mobile sinks in cluster based UWSNs for oil pipeline leakage detection

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Energy efficient and completely reliable data gathering in resource constrained sparse Underwater Wireless Sensor Networks (UWSNs) is challenging and requires dedicated routing techniques. Routing having mobility assistance employs a Mobile Sink (MS) or a mobile relay for data gathering. It mitigates transmission power consumption as well as relaying overhead. But multiple MSs should be deployed in order to reduce the load of a single MS. The visiting schedule of each MS should consider the priority of data, data gathering delay and buffer overflow of each sensor. In order to address these issues, a priority-based data gathering scheme using multiple MSs for clustered UWSN is proposed to help in pipeline leakage detection under the water. In this work, each MS is deployed in such a way so that it can move in both the directions i.e., top to bottom or bottom to top. When a Cluster Head (CH) receives critical data, it sends an emergency notification to the nearest MS via other CHs. Upon receiving the emergency notification, MS immediately visits that CH to gather the critical data (oil leakage).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

Yes.

Code availability

Not applicable.

Abbreviations

\({CH}_{i}^{l}\) :

CH of level l, i = 1, 2, …, n, and l = 1, 2, …, L

CM j :

Cluster member, j = 1, 2, …, .m

m j, m i :

Data transmission time slots of CMji and CHi, i > j

MS k :

kth mobile sink

VT k :

Visiting time of MSk to any CH

DL i :

Buffer overflow dead line of CHi

D type :

Data type

CR :

Critical or emergency data

NCR :

Non critical or normal data

M EN :

Emergency notification message

L :

Number of hierarchical levels of clusters

SCH ij :

TDMA schedule of jth member of cluster i

T s :

Time slot

References

  1. Lanbo, L., Shengli, Z., Jun-Hong, C.: Prospects and problems of wireless communication for underwater sensor networks. Wirel. Commun. Mob. Comput. 8(8), 977–994 (2008). https://doi.org/10.1002/wcm.654

    Article  Google Scholar 

  2. Shao, Z., Feng, S.: Research on discontinuous guidance and hardware-in-the-loop simulation for unmanned underwater vehicle. Clust. Comput. 22(4), 7975–7982 (2019). https://doi.org/10.1007/s10586-017-1545-5

    Article  MathSciNet  Google Scholar 

  3. LI-COR Biosciences—impacting lives through science. Licor.com. (2021). Retrieved 12 January 2021, from https://www.licor.com/env/products/light/quantum_underwater

  4. Awan, K.M., Shah, P.A., Iqbal, K., Gillani, S., Ahmad, W., Nam, Y.: Underwater wireless sensor networks: a review of recent issues and challenges. Wirel. Commun. Mob. Comput. (2019). https://doi.org/10.1155/2019/6470359

    Article  Google Scholar 

  5. Javaid, N., Jafri, M.R., Khan, Z.A., Qasim, U., Alghamdi, T.A., Ali, M.: IAMCTD: Improved adaptive mobility of courier nodes in threshold-optimized DBR protocol for underwater wireless sensor networks. Int. J. Distrib. Sens. Netw. 10(11), 213012 (2014). https://doi.org/10.1155/2014/213012

    Article  Google Scholar 

  6. Jahanbakht, M., Xiang, W., Hanzo, L., Azghadi, M.R.:Internet of underwater things and big marine data analytics—a comprehensive survey (2020) arXiv preprint. arXiv:2012.06712

  7. Wang, X., Qin, D., Zhao, M., Guo, R., Berhane, T.M.: UWSNs positioning technology based on iterative optimization and data position correction. EURASIP J. Wirel. Commun. Netw. 2020(1), 1–19 (2020). https://doi.org/10.1186/s13638-020-01771-9

    Article  Google Scholar 

  8. Hafeez, S., Wong, M.S., Abbas, S., Kwok, C.Y., Nichol, J., Lee, K.H., et al.: Detection and monitoring of marine pollution using remote sensing technologies. In: Monitoring of Marine Pollution. IntechOpen, London (2018). https://doi.org/10.5772/intechopen.81657

    Chapter  Google Scholar 

  9. Zhang, L., Zhang, L., Liu, S., Zhou, J., Papavassiliou, C.: Low-level control technology of micro autonomous underwater vehicle based on intelligent computing. Clust. Comput. 22(4), 8569–8580 (2019). https://doi.org/10.1007/s10586-018-1909-5

    Article  Google Scholar 

  10. Fattah, S., Gani, A., Ahmedy, I., Idris, M.Y.I., Targio Hashem, I.A.: A survey on underwater wireless sensor networks: requirements, taxonomy, recent advances, and open research challenges. Sensors 20(18), 5393 (2020). https://doi.org/10.3390/s20185393

    Article  Google Scholar 

  11. Gupta, O., Goyal, N.: The evolution of data gathering static and mobility models in underwater wireless sensor networks: a survey. J. Ambient. Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-020-02719-z

    Article  Google Scholar 

  12. Wan, Z., Liu, S., Ni, W., Xu, Z.: An energy-efficient multi-level adaptive clustering routing algorithm for underwater wireless sensor networks. Clust. Comput. 22(6), 14651–14660 (2019). https://doi.org/10.1007/s10586-018-2376-8

    Article  Google Scholar 

  13. Haque, K.F., Kabir, K.H., Abdelgawad, A.: Advancement of routing protocols and applications of underwater wireless sensor network (UWSN)—a survey. J. Sens. Actuator Netw. 9(2), 19 (2020). https://doi.org/10.3390/jsan9020019

    Article  Google Scholar 

  14. Jalaja, M.J., Jacob, L.: On-demand data collection in sparse underwater acoustic sensor networks using mobile elements. In: Proceedings of the 10th International Conference on Wireless and Mobile Communication (ICWMC'14), pp. 105–111 (2014). https://www.semanticscholar.org/paper/On-Demand-Data-Collection-in-Sparse-Underwater-Se-JalajaM.-Jacob/5562ac210bcfe83a822eb87af5e3e040bcb62c40

  15. Khan, J.U., Cho, H.S.: A distributed data-gathering protocol using AUV in underwater sensor networks. Sensors 15(8), 19331–19350 (2015). https://doi.org/10.3390/s150819331

    Article  Google Scholar 

  16. Alfouzan, F.A., Ghoreyshi, S.M., Shahrabi, A., Ghahroudi, M.S.: A novel cross-layer mobile data-gathering protocol for underwater sensor networks. In: Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), pp. 1–6. IEEE (2020). https://doi.org/10.1109/VTC2020-Spring48590.2020.9129135

  17. Ilyas, N., Alghamdi, T.A., Farooq, M.N., Mehboob, B., Sadiq, A.H., Qasim, U., et al.: AEDG: AUV-aided efficient data gathering routing protocol for underwater wireless sensor networks. In: Proceedings of the ANT/SEIT, pp. 568–575 (2015). https://doi.org/10.1016/j.procs.2015.05.038

  18. Favaro, F., Casari, P., Guerra, F., Zorzi, M.: Data upload from a static underwater network to an AUV: Polling or random access?. In: Proceedings of the Oceans-Yeosu, pp. 1–6. IEEE.(2012). https://doi.org/10.1109/OCEANS-Yeosu.2012.6263499

  19. Huang, C.J., Wang, Y.W., Lin, C.F., Chen, Y.T., Chen, H.M., Shen, H.Y., et al.: A self-healing clustering algorithm for underwater sensor networks. Clust. Comput. 14(1), 91–99 (2011). https://doi.org/10.1007/s10586-010-0139-2

    Article  Google Scholar 

  20. Fotohi, R., Nazemi, E., Aliee, F.S.: An agent-based self-protective method to secure communication between UAVs in unmanned aerial vehicle networks. Veh. Commun. (2020). https://doi.org/10.1016/j.vehcom.2020.100267

    Article  Google Scholar 

  21. Gupta, O., Goyal, N., Anand, D., Kadry, S., Nam, Y., Singh, A.: Underwater networked wireless sensor data collection for computational intelligence techniques: issues, challenges, and approaches. IEEE Access 8, 122959–122974 (2020). https://doi.org/10.1109/ACCESS.2020.3007502

    Article  Google Scholar 

  22. Xiao, Y., Zhang, Y., Gibson, J.H., Xie, G.G., Chen, H.: Performance analysis of ALOHA and p-persistent ALOHA for multi-hop underwater acoustic sensor networks. Clust. Comput. 14(1), 65–80 (2011). https://doi.org/10.1007/s10586-009-0093-z

    Article  Google Scholar 

  23. Khan, F.A., Khan, S.A., Turgut, D., Bölöni, L.: Scheduling multiple mobile sinks in Underwater Sensor Networks. In: Proceedings of the IEEE 40th Conference on Local Computer Networks (LCN), pp. 149–156. IEEE (2015). https://doi.org/10.1109/LCN.2015.7366294

  24. Khan, J.U., Cho, H.S.: A data gathering protocol using AUV in underwater sensor networks. In: Proceedings of the OCEANS 2014-TAIPEI, pp. 1–6. IEEE (2014). https://doi.org/10.1109/OCEANS-TAIPEI.2014.6964549

  25. Khan, J.U., Cho, H.S.: A multihop data-gathering scheme using multiple AUVs in hierarchical underwater sensor networks. In: Proceedings of the International Conference on Information Networking (ICOIN), pp. 265–267. IEEE (2016). https://doi.org/10.1109/ICOIN.2016.7427074

  26. Akbar, M., Javaid, N., Khan, A.H., Imran, M., Shoaib, M., Vasilakos, A.: Efficient data gathering in 3D linear underwater wireless sensor networks using sink mobility. Sensors 16(3), 404 (2016). https://doi.org/10.3390/s16030404

    Article  Google Scholar 

  27. Kartha, J.J., Jacob, L.: Network lifetime-aware data collection in underwater sensor networks for delay-tolerant applications. Sādhanā 42(10), 1645–1664 (2017). https://doi.org/10.1007/s12046-017-0713-x

    Article  MathSciNet  MATH  Google Scholar 

  28. Aldosari, H., Elfouly, R., Ammar, R., Alsulami, M.: Performance of new monitoring architectures for underwater oil/gas pipeline using hyper-sensors. In: Proceedings of the 2020 IEEE Symposium on Computers and Communications (ISCC), pp. 1–6. IEEE (2020). https://doi.org/10.1109/ISCC50000.2020.9219687

  29. Liu, C., Liao, Y., Wang, S., Li, Y.: Quantifying leakage and dispersion behaviors for sub-sea natural gas pipelines. Ocean Eng. 216, 108107 (2020). https://doi.org/10.1016/j.oceaneng.2020.108107

    Article  Google Scholar 

  30. Zhao, X., Wang, X., Du, Z.: Research on detection method for the leakage of underwater pipeline by YOLOv3. In: Proceedings of the 2020 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 637–642. IEEE (2020). https://doi.org/10.1109/ICMA49215.2020.9233693

  31. Hayder, I.A., Khan, S.N., Althobiani, F., Irfan, M., Idrees, M., Ullah, S., et al.: Towards controlled transmission: a novel power-based sparsity-aware and energy-efficient clustering for underwater sensor networks in marine transport safety. Electronics 10(7), 854 (2021). https://doi.org/10.3390/electronics10070854

    Article  Google Scholar 

  32. Goyal, N., Dave, M., Verma, A.K.: Energy efficient architecture for intra and inter cluster communication for underwater wireless sensor networks. Wireless Pers. Commun. 89(2), 687–707 (2016). https://doi.org/10.1007/s11277-016-3302-0

    Article  Google Scholar 

  33. Goyal, N., Dave, M., Verma, A.K.: Improved data aggregation for cluster based underwater wireless sensor networks. Proc. Natl. Acad. Sci. India Sect. A 87(2), 235–245 (2017). https://doi.org/10.1007/s40010-017-0344-y

    Article  Google Scholar 

  34. Goyal, N., Dave, M., Verma, A.K.: Trust model for cluster head validation in underwater wireless sensor networks. Underwater Technol (2017). https://doi.org/10.3723/ut.34.107

    Article  Google Scholar 

  35. Jamshidi, V., Nekoukar, V., Refan, M.H.: Real time UAV path planning by parallel grey wolf optimization with align coefficient on CAN bus. Clust. Comput. (2021). https://doi.org/10.1007/s10586-021-03276-6

    Article  Google Scholar 

Download references

Funding

No.

Author information

Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Nitin Goyal, Ashok Kumar & Renu Popli

  2. National Institute of Technology, Hamirpur, Himachal Pradesh, India

    Lalit Kumar Awasthi

  3. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Nonita Sharma

  4. Seth Jai Parkash Mukand Lal Institute of Engineering and Technology, Yamunanagar, Haryana, India

    Gaurav Sharma

Authors
  1. Nitin Goyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashok Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Renu Popli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lalit Kumar Awasthi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nonita Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gaurav Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

NG, AK conceived the idea, designed the experiments and analysed the data; RP, NS performed experiments and conducted the analysis; LKA analysed the methods, interpreted the results. GS drew conclusions, and proofread the paper. All the authors agree with the above contributions.

Corresponding author

Correspondence to Nitin Goyal.

Ethics declarations

Conflict of interest

The author declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goyal, N., Kumar, A., Popli, R. et al. Priority based data gathering using multiple mobile sinks in cluster based UWSNs for oil pipeline leakage detection. Cluster Comput 25, 1341–1354 (2022). https://doi.org/10.1007/s10586-021-03513-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03513-y

Keywords

Search

Navigation